ArticleJournal of healthcare engineering
Study on the Grading Model of Hepatic Steatosis Based on Improved DenseNet.
Article in Journal of healthcare engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To achieve intelligent grading of hepatic steatosis, a deep learning-based method for grading hepatic steatosis was proposed by introducing migration learning in the DenseNet model, and the effectiveness of the method was verified by applying it to the practice of grading hepatic steatosis. The results show that the proposed method can significantly reduce the number of model iterations and improve the model convergence speed and prediction accuracy by introducing migration learning in the deep learning DenseNet model, with an accuracy of more than 85%, sensitivity of more than 94%, specificity of about 80%, and good prediction performance on the training and test sets. It can also detect hepatic steatosis grade 1 more accurately and reliably, and achieve automated and more accurate grading, which has some practical application value.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.